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Monthly Archives: May 2017

There is growing interest in who will dominate the self-driving future: car companies, ride-sharing companies or someone else? My bet is on ride-sharing companies, and the reason is that I think that the self-driving car industry will require expertise that currently resides with ride-sharing companies. Furthermore, the kinds of things car companies have traditionally differentiated themselves on will not matter very much, pushing them into a commodity role.

**Important full disclosure: A close family member of mine works for a ride-sharing company.**

On the consumer side, I suspect that in the future people will think about the manufacturer of the self-driving vehicle they are in as much as they think about the manufacturer of the bus, train car or airplane they are in now, which is to say “very little.” They will not care about styling, aesthetics, mileage, maintenance, parts availability, re-sale value, and service costs, what it says about them as a person to own one, and so on. All things that would be car-buyers care about a great deal will matter very little. There’s a reason there’s no magazine called “Bus & Passenger.”

Some of these vehicle attributes will affect *costs* to platforms—and buyers of fleets of cars will presumably try to buy high quality at low cost, but they aren’t going to pay a premium for design or a brand. The self-driving world is going to be an ugly, commodified world focused on cost and reliability.

Customers *will* care about fleet reputations for cleanliness, timeliness, routing quality, customer service support, safety, and so on—the things they care about with airlines—but these are things that will be determined more by the *operational* excellent of whoever manages the fleet. Unfortunately for car makers, operations is not their forte, whereas the ride-sharing companies are extremely operations/customer-service focused.

Ride-sharing companies have expertise in customer service, marketing, analytics, developing maps, developing routing algorithms, creating software interfaces, and fighting regulatory/legal battles. All of these things are still likely to matter a great deal in the self-driving future. The customer service aspect in an area where these companies are actually quite different form the Silicon Valley mold—they deal with paying customers constantly in a high-touch way, unlike say Google or Facebook.

Ride-sharing companies don’t have expertise in managing fleets of cars (fueling, cleaning, repair and so on), but neither do car companies. But even then, this part of the business doesn’t seem that hard relative to the others, and there are lots of companies and people with this expertise (UPS, FedEx, Hertz, Avis etc.). Car manufacturers have almost no experience with operations. And they have almost no experience dealing with customers.

On the actual self-driving technology side, to extent that improvements will be driven mainly by more and better data rather than hardware, the ride-sharing companies are also the ones well-poised to collect the most data about driving under actual and varied conditions in the long run up to a fully-automated future.

Perhaps the biggest advantage held by ride-sharing companies is that they have a very natural way to transition to the future—start slipping self-driving vehicles into the mix, pacing the introduction as the technology develops. In contrast, the car companies (or Google/Waymo) essentially need to clone Uber or Lyft functionality, but do it with an unproven technology from day one. This will be very hard. Instead, car companies will largely choose to partner with ride-sharing companies, but the ride-sharing companies will have lots of manufacturer options to choose from, and are not going to give away the company to do so, or recklessly form exclusive partnerships.

One might argue that you can’t have a fleet without a car maker willing to sell to you, but there are many, many car markers out there. Just because the car is an essential component of the productive process doesn’t mean that the maker of that input will control everything—McDonalds isn’t a subsidiary of a stove company.

Anyway, for course this is all highly speculative and maybe I’ll wistfully read this naive blog post from my Ford-brand self-driving vehicle in a few years, but I think it’s more likely it will be an Uber/Lyft/Didi/Ola/Gett and some nameless, white-label vehicle made by Honda.

Today I attended the 5th year anniversary celebration for MSR NYC. There was a great group of speakers and panelists—I’m super impressed by what MSR has accomplished. One topic that came up at several points during the day was the labor market effects of technological developments—particularly that powerful AI might displace many workers.

Economists have traditionally been sanguine about the effects of technological change on the labor market, viewing widespread technological unemployment as unlikely. This perspective is based on the historical experience of substantial technological change not having persistent disemployment effects. However, it has been pointed out that we have one vivid example where this optimism has not been warranted—what I call the parable of the horse.

The story is that the internal combustion engine came along and horses saw their marginal product decline below the cost of their feed and so horses disappeared, at least in the “labor” market. This is undoubtedly true—the figure below shows the number of horses (and mules) in the US (from The Demographics of the U.S. Equine Population). The implication is that today’s “horses”—low skilled labor or maybe even labor in general—will disappear as AI can do more and more tasks.

I find the horse parable interesting, but unpersuasive—at least with respect to how it is likely to affect relatively low-skilled workers—because I think the analogy misses the reason why horses fared so poorly. The problem was not that they were “low skilled” but rather their extreme specialization. Horses did one job and one job only—exerting physical force, which could be used to pull or push things. That they could be almost entirely displaced by a superior pushing/pulling technology is, in some sense, not surprising. But what I think is important in the human labor market is that being able to do one thing—and one thing only—is typically a characteristic of high skilled labor, not low skilled labor.

Most low-skilled labor is no longer like horse labor, in that the low-skilled jobs that exist now are those that require some mix of physical, intellectual and even “emotional” skills. This mix makes full automation challenging. But even when some specific job does “fall” to automation, there is a still a very large pool of remaining jobs that could still need to be done and require relatively little skill or new training, by definition of being low-skilled. In short, one advantage of the low skilled labor market is that there are lots of jobs you qualify for. The downside, of course, is that precisely because lots of people can qualify for those jobs and so wages are low. The workers that are vulnerable to technical change—in the sense that they are likely to experience large declines in income—are those workers with highly specialized skills.

Truck driving as a low-skilled example

To give a more concrete example, consider the job of truck driver, which might be on the automation chopping block. First, many people with the job “truck driver” are actually some combination of sales representative, inventory-taker, first line mechanic, warehouse worker, forklift operator and so on. As such, it is far from obvious that even if substantial amounts of driving end up being done through automation that labor demand for “truck driver” would fall.

Second, even if the truck driver occupation sees a large negative demand shock, what other jobs could a truck driver do that pay about the same? Well, let’s look at the BLS occupational data. The table below shows the most recent BLS occupational data, w/ US employment totals and average hourly wages, sorted by average hourly wage. I restricted the list to occupations with more than 500K employees. We can see that being a light truck driver (i.e., not a heavy truck transporting freight or heavy equipment) pays about $16.50/hour, which is below the median wage in the US but still substantially higher than the minimum wage.

We can see that both above and below, there are a number of jobs that are plausible substitute occupations for a displaced truck driver. For example, of those below, most require no formal education or certification, perhaps except for substitute teachers or medical assistants. If we go higher, we start to see jobs that require more skills or that are more physically taxing or dangerous (e.g., construction laborer), but are still reasonable substitutes. For example, many truck drivers are also decent mechanics and perhaps with some more training, could find work as “Maintenance and Repair Workers, General.”

Not only do displaced truck drivers have lots of “nearby” occupations that pay about the same with little additional human capital requirements, the displaced drivers are not likely to drive down wages very much in their new occupations. There are, of course, lots of truck drivers, but if they split into a reasonably large chunk of other occupations, the new entrants would not be much of a supply shock.

A specialized occupation example

Now we’ll look at a more specialized occupation. Let’s consider accountants and auditors (even this one still seems far away from being even remotely automatable). It pays quite nicely and requires substantial specialized skill. If we look at nearby jobs, very few would be open to a displaced accountant without substantial re-training.

Secondary School Teachers, Except Special and Career/Technical Education

962,820

*

Accountants are the “horses” here—the ones that are vulnerable to large drop offs in earnings because of their specialization. Fortunately, if we care about inequality, this is the “right” group to be affected. Because of their existing financial wealth and their general human capital, they are likely better able to deal with the disequilibria created by technological change.